关键词：
elastic waves;pseudospin-Hall edge states;topological metamaterials;valley-Hall edge states
摘要：
Topological metamaterials protected by the spatial inversion symmetry mainly support single type edge state, interpreted by either the quantum valley Hall effect or the quantum spin Hall effect. However, owing to the existence of the complicated couplings and waveform conversions during elastic wave propagation, realizing topologically protected edge states that support both pseudospin and valley degrees of freedom in elastic system remains a great challenge. Here, we propose a two-dimensional Kekulé phononic crystal (PC) that can simultaneously possess pseudospin- and valley-Hall edge states in different frequency bands. By inhomogeneously changing the elliptical direction in a Kekulé lattice of elliptical cylinders, three complete phononic bandgaps exhibiting distinct topological phase transitions can be obtained, one of which supports a pair of pseudospin-Hall edge states and the other hosts valley-Hall edge states in the low and high frequency regime. Furthermore, a sandwiched PC heterostructure and a four-channel cross-waveguide splitter are constructed to achieve selective excitation and topological robust propagation of pseudospin- and valley-momentum locking edge states in a single configuration. These results provide new possibilities for manipulating in-plane bulk elastic waves with both pseudospin and valley degrees of freedom in a single configuration, which has potential applications for multiband and multifunctional waveguiding.
摘要：
As an adaptive signal decomposition method, symplectic geometry mode decomposition (SGMD) method is suitable for dealing with non-stationary signals However, the decomposition effect is not ideal when dealing with rolling bearing fault signals with strong background noise. On the one hand, this noise reduction method of SGMD is not suitable for fault signals with strong background noise. On the other hand, SGMD uses QR decomposition method, which results in decomposition error diffusion in the decomposition of singular matrix. Therefore, an enhanced symplectic characteristics mode decomposition (ESCMD) method is proposed in this paper. ESCMD enhances fault features through the calculus operator to make fault features easier to extract, and replaces QR decomposition with eigenvalue decomposition (EVD) to avoid error diffusion during matrix decomposition. Emulational and experimental results show that ESCMD has excellent noise robustness and feature enhancement performance. (C) 2020 Published by Elsevier Ltd.
通讯机构：
[Wang, Rongji] C;Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
Deep stacking l(1)-norm center configuration convex hull;Center configuration;Rolling bearing;Fault diagnosis
摘要：
Maximum margin classifier with flexible convex hulls (MMC-FCH) is an adaptive pattern recognition method based on convex hull vector and shrinkage factor, which can effectively identify different fault states. However, MMC-FCH is a shallow learning algorithm that cannot effectively diagnose complex signals. Meanwhile, MMC-FCH is essentially a binary classifier. For multi-classification, MMC-FCH can only perform multiple binary classifications. To overcome the shortcomings of MMC-FCH, we propose a deep stacking center configuration convex hull ((DSCH)-H-3), which combines the convex hull with the idea of stacking-based representation learning (SRL). In (DSCH)-H-3, the output of all previous modules combine with the original signal as the input of the next module to fully learn the information in the original signal. At the same time, the concept of center configuration is used to construct the multi-classification objective function of center configuration convex hull ((CH)-H-3). However, redundant information and noise information may still exist in the proposed (DSCH)-H-3 method. Therefore, we further propose a deep stacking l(1)-norm center configuration convex hull (DSl(1)C(3)H) method, which makes the prediction model more robust and sparser under the constraint of l(1)-norm distance. The experiments of rolling bearings show that the proposed DSl(1)C(3)H method has better classification performance. (C) 2019 Elsevier Ltd. All rights reserved.
摘要：
For the feature tensor of multi-sensor signals classification problem in gear intelligent fault diagnosis, a new tensor classifier named nearest neighbor convex hull tensor classification (NNCHTC) is proposed in this paper. First, the convex hull distance from a test tensor sample to the convex hull is taken as the similarity measure for classification. Then, the convex hull distance calculation is transformed into the feature tensor inner product, and CANDECOMP/PARAFAC (CP) decomposition is applied to the calculation process to capture the intrinsic information of the feature tensor. Furthermore, the reduction factor is introduced into NNCHTC to enhance its robustness. Finally, feature tensors are obtained from multi-sensor signals by wavelet packet transform (WPT) and used to identify gear working condition by NNCHTC. The experimental results show that NNCHTC not only can be effectively applied to the gear intelligent fault diagnosis based on multi-sensor signals but also has better robustness.
通讯机构：
[Wang, Rongji] C;Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
back propagation artificial neural network;injection molding process;process parameters;warpage
摘要：
A back propagation artificial neural network (BPANN) prediction model for warpage of injection-molded polypropylene was developed based on an orthogonal design method. The BPANN model was trained by the input and output data obtained from the moldflow software platform simulations. It is proved that the BPANN model can predict the warpage with reasonable accuracy. Utilizing the BPANN model, the effects of the process parameters, packing pressure (Pp), melt temperature (Tme), mold temperature (Tmo), packing time (tp), cooling time (tc), and fill pressure (pf), on the warpage were investigated. The most important process parameter affecting the warpage was Pp, and the second most important was Tme. The rest of the process parameters, Tmo, tp, tc, and pf, were found to be relatively less influential. Warpage increased with elevating Tmo. In contrast, an increase in Pp and Tme caused the warpage to decrease.
期刊：
INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY,2013年46(2-3):124-140 ISSN：0268-1900
通讯作者：
Wang, Rong Ji
作者机构：
[Wang, Rong Ji; Tan, Wen Fang] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.;[Zhou, Dian-Wu] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China.
通讯机构：
[Wang, Rong Ji] C;Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
squeeze casting;process parameter;artificial neural network;ANN;solidification time
摘要：
Based on artificial neural network (ANN) and ProCast software, the effects of different process parameter on the solidification time of squeeze casting hot die steel were investigated, such as interfacial heat transfer coefficient of metal/cavity die (h1), applied pressure (Pa), interfacial heat transfer coefficient of metal/male die (h2), die pre-heat temperature (Td) and pouring temperature (Tp). An ANN model on the relationship between process parameters and solidification time was constructed. The test results show that the ANN model is reasonable and can accurately predict the solidification time and the influence of process parameters on solidification time. The most important parameter is Td, and the secondary is Tp. While Td and Tp increasing within a certain range, the solidification time is found to increase, in contrast, Pa causes the solidification time to decrease. However, h1 and h2 increasing within a certain range, the solidification time is found to decrease. Moreover, the solidification time increases rapidly when h1 and h2 are above their respective critical point. The critical value increases with an increase in mould thickness.
